Bias compensated pseudolinear Kalman filter (BC-PLKF) has been shown to solve the bias problem of pseudolinear Kalman filter (PLKF) and outperform Extended Kalman Filter (EKF) and many others in bearings-only target estimation applications with a low computational cost. However, BC-PLKF assumes that measurement noise is white, which is not a valid approximation for some applications such as weather-vane-used guided missiles where wind disturbance appears as a strongly time-correlated measurement noise, estimators performing high-frequency measurement updates, or cascaded Kalman filter-based algorithms. When the well-known noise augmentation method is applied to BC-PLKF, no straightforward solution for the bias compensation is available. First, process noise and observer matrix become coupled leading to unique bias. Second, the measurement autocovariance turns into zero whose inverse is used at the bias compensation step of BC-PLKF. Therefore, a bias analysis is performed for PLKF where the measurement noise is colored. Moreover, the generalized BC-PLKF algorithm (GBC-PLKF) for colored noise-corrupted measurements is derived. Simulations are performed to compare performances of GBC-PLKF, EKF, Cubature Kalman Filter (CKF), BC-PLKF, and colored noise augmented EKF and CKF (C-EKF and C-CKF) with typical air-to-surface missile engagement scenarios. Results verify that GBC-PLKF outperforms all comparison filters with a low computational cost for bearings-only estimation applications.

偏置补偿伪线性卡尔曼滤波器 (BC-PLKF) 已被证明可以解决伪线性卡尔曼滤波器 (PLKF) 的偏置问题，并以低计算成本在仅轴承目标估计应用中优于扩展卡尔曼滤波器 (EKF) 和许多其他滤波器。然而，BC-PLKF 假设测量噪声是白色的，这对于某些应用来说不是有效的近似值，例如风向使用的导弹，其中风干扰表现为与时间强相关的测量噪声，估计器执行高频测量更新，或级联基于卡尔曼滤波器的算法。当众所周知的噪声增强方法应用于 BC-PLKF 时，没有直接的偏置补偿解决方案可用。首先，过程噪声和观察者矩阵耦合导致独特的偏差。第二，测量自协方差变为零，其倒数用于 BC-PLKF 的偏置补偿步骤。因此，对测量噪声带有颜色的 PLKF 进行了偏差分析。此外，还导出了用于彩色噪声破坏测量的广义 BC-PLKF 算法 (GBC-PLKF)。执行模拟以比较 GBC-PLKF、EKF、Cubature 卡尔曼滤波器 (CKF)、BC-PLKF 以及有色噪声增强 EKF 和 CKF（C-EKF 和 C-CKF）与典型空对地导弹交战场景的性能. 结果验证了 GBC-PLKF 以较低的计算成本优于所有比较滤波器，用于仅轴承估计应用程序。推导出用于有色噪声损坏测量的广义 BC-PLKF 算法 (GBC-PLKF)。执行模拟以比较 GBC-PLKF、EKF、Cubature Kalman 滤波器 (CKF)、BC-PLKF 以及有色噪声增强 EKF 和 CKF（C-EKF 和 C-CKF）与典型空对地导弹交战场景的性能. 结果验证了 GBC-PLKF 以较低的计算成本优于所有比较滤波器，用于仅轴承估计应用程序。推导出用于有色噪声损坏测量的广义 BC-PLKF 算法 (GBC-PLKF)。执行模拟以比较 GBC-PLKF、EKF、Cubature Kalman 滤波器 (CKF)、BC-PLKF 以及有色噪声增强 EKF 和 CKF（C-EKF 和 C-CKF）与典型空对地导弹交战场景的性能. 结果验证了 GBC-PLKF 以较低的计算成本优于所有比较滤波器，用于仅轴承估计应用程序。